Vestib_09202017

  This explores potential numeric cutoff of central signs in the diagnosis of central dysfunction. Data obtained from 07182017 Data Request was utilized for this analysis.

Demographic Information

Total Patients: 743 Time in Service: 18.14 ± 6.56 Average Patient Age: 38.85 ± 6.56

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Patient Age Labeled:

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Age_lbl	N	Percent
20 to 24	12	1.62
25 to 29	61	8.21
30 to 34	112	15.07
35 to 39	189	25.44
40 to 44	230	30.96
45 to 49	104	14.00
50 to 54	29	3.90
55 to 60	6	0.81

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Gender Counts:

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Gender	N	Percent
M	731	98.38
F	12	1.62

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Service Counts:

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Service	N	Percent
Navy	456	61.37
Army	182	24.50
Marines	60	8.08
Air Force	44	5.92
Coast Guard	1	0.13

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Patient Complaints

  The details of complaints are listed below. Total number of patients who exhibited each complaint as well as the percentage of patients with said complaint.

Average Number of Patient Symptoms: 2.24 ± 1.31

Symptoms	N	Perc
Dizziness_lightheadedness	555	71.80
Disequilibrium_imbalance	440	56.92
Vertigo_Spinning	263	34.02
Motion_Sensitivity	220	28.46
Syncope_pre_syncope	185	23.93

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Central Counts

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Average Central Counts: 2.24 ± 1.97 Median Central Counts: 2 Total Number of Central Tests: ~17

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Total_C	N	CumN	Perc	CumPerc
13	1	1	0.13	0.13
11	1	2	0.13	0.26
9	3	5	0.40	0.66
8	4	9	0.54	1.20
7	15	24	2.02	3.22
6	26	50	3.50	6.72
5	55	105	7.40	14.12
4	68	173	9.15	23.27
3	101	274	13.59	36.86
2	152	426	20.46	57.32
1	163	589	21.94	79.26
0	154	743	20.73	99.99

Central Signs

  Below outlines the total number of patients with central findings per assessment. The percent is the percentage of patients who exhibited a central sign for that test.

Test	Sum	Perc
Oculomotor_Smooth_Pursuit_Vertical	481	62.23
Oculomotor_Smooth_Pursuit_Horizontal	201	26.00
Oculomotor_Saccades_Vertical	189	24.45
VNGTest_DixHallpike_Head_Right	127	16.43
VNGTest_DixHallpike_Head_Left	121	15.65
VNGTest_Roll_Test_Lateral_Left	119	15.39
VNGTest_Roll_Test_Lateral_Right	106	13.71
Oculomotor_Gaze_Right	79	10.22
Oculomotor_Gaze_Left	79	10.22
Oculomotor_Saccades_Horizontal	75	9.70
Oculomotor_Gaze_Up	44	5.69
Oculomotor_Gaze_Center	31	4.01
Oculomotor_Gaze_Down	29	3.75
Oculomotor_Optokinetic	23	2.98
VNGTest_Horizontal_Head_Shake	20	2.59
Oculomotor_Spontaneous_Nystagmus	18	2.33
Positioning_supine_Gaze_up	7	0.91
Positioning_supine_Gaze_Center	1	0.13
VNGTest_Roll_Test_Body_Right	1	0.13

Variable Exploration

  Below are scatterplots which compare the various variables of interest. While it is interesting to see correlations among the variables, which is most significant is how these variables respond to the changes in Total_C (Total Central Signs). From here onward, we will try to use extraneous variables to compare variances within the data.

Demographics Against Total Central

  Scatterplot displaying relationships among Injury and other demographics against Total_C.

Surveys Against Total Central

  Below displays the scatterplots of the various surveys against the total number of central signs. Also, should be noted the distribution of the tests within the diagonal.

Individual Survey Questions

&nbs; P, Fu, and E come from the DHI subscales - Physical, Funcional, and Emotional.

Changes in Means per Cutoffs

  We will compare the mean differences of surveys among our sample population with various cutoffs for Central Dysfunction. The values calculated below are ‘Normal Patient Means’ - ‘Central Patient Means’. Dependent upon the survey, a (-) mean difference signifies that Central Patients performed worse on that particular survey.

Surveys

Addtl Surveys

Specific Survey Questions

Variable Significance Testing

Variance Comparison

  Once we have determined the suggested cutoff for our grouping, we will examine the structure of the variables when comparing these two groups. Variance comparisons has been commented out. We’ll first explore other methods of explaining variance.

Regression Exploration

  Utilizing a regression, we can evaluate which variables contribute most to the variance exhibited within the total number of Central Signs. After scaling the variables, it appears that SOT and NSI explain much of the variance within Total Central signs. Additional exploration will be conducted on these variables, as well as the DHI, ABC and FGA; surveys/questionnaires which hope to provide insight into the varability exhibited in the total number of central signs.

## Stepwise Model Path 
## Analysis of Deviance Table
## 
## Initial Model:
## Total_C ~ ABC_Tot_Score + NSI_Tot_Score + PCLM_Tot_Score + PHQ9_Tot_Score + 
##     GAD7_Tot_Score + DHI_Tot_Score + FGA + SOT + LOC + AOC + 
##     PTA + TimeInService + symp_tot
## 
## Final Model:
## Total_C ~ NSI_Tot_Score + SOT + LOC
## 
## 
##                Step Df   Deviance Resid. Df Resid. Dev      AIC
## 1                                       416   1660.911 609.0745
## 2             - FGA  1 0.02056280       417   1660.931 607.0799
## 3  - PHQ9_Tot_Score  1 0.04407690       418   1660.975 605.0913
## 4   - TimeInService  1 0.05785070       419   1661.033 603.1063
## 5        - symp_tot  1 0.12849741       420   1661.162 601.1395
## 6   - DHI_Tot_Score  1 0.09796185       421   1661.260 599.1649
## 7             - PTA  1 0.23689418       422   1661.497 597.2262
## 8  - GAD7_Tot_Score  1 0.21278788       423   1661.709 595.2813
## 9             - AOC  1 1.30399859       424   1663.013 593.6186
## 10  - ABC_Tot_Score  1 2.48008364       425   1665.493 592.2593
## 11 - PCLM_Tot_Score  1 3.05270838       426   1668.546 591.0468

##  ABC_Tot_Score  NSI_Tot_Score PCLM_Tot_Score PHQ9_Tot_Score GAD7_Tot_Score 
##    0.022645624    0.232795310    0.043104926    0.045133686    0.033239361 
##  DHI_Tot_Score            FGA            SOT            LOC            AOC 
##    0.046782165    0.036470915    0.306744857    0.153372132    0.034368683 
##            PTA  TimeInService       symp_tot 
##    0.002708148    0.001791970    0.040842222

## Stepwise Model Path 
## Analysis of Deviance Table
## 
## Initial Model:
## Total_C ~ NSI_Q1 + NSI_Q2 + NSI_Q3 + NSI_Q4 + NSI_Q5 + NSI_Q6 + 
##     P + Fu + E + ABC_Tot_Score + FGA + SOT + LOC + AOC + PTA + 
##     TimeInService + symp_tot
## 
## Final Model:
## Total_C ~ NSI_Q2 + NSI_Q4 + SOT + LOC
## 
## 
##               Step Df   Deviance Resid. Df Resid. Dev        AIC
## 1                                      412   399.1904   4.030907
## 2              - P  1 0.05410681       413   399.2445   2.089186
## 3  - TimeInService  1 0.08165149       414   399.3262   0.177118
## 4  - ABC_Tot_Score  1 0.08575610       415   399.4119  -1.730549
## 5         - NSI_Q1  1 0.13433482       416   399.5462  -3.585950
## 6            - PTA  1 0.27589665       417   399.8221  -5.289127
## 7            - FGA  1 0.31264513       418   400.1348  -6.953015
## 8       - symp_tot  1 0.38134908       419   400.5161  -8.543398
## 9         - NSI_Q5  1 0.55109214       420   401.0672  -9.952145
## 10           - AOC  1 0.55738117       421   401.6246 -11.354969
## 11             - E  1 0.89831771       422   402.5229 -12.394258
## 12            - Fu  1 0.38645614       423   402.9094 -13.981619
## 13        - NSI_Q6  1 1.09987429       424   404.0093 -14.809392
## 14        - NSI_Q3  1 1.47928453       425   405.4885 -15.237817

##        NSI_Q1        NSI_Q2        NSI_Q3        NSI_Q4        NSI_Q5 
##   0.029044018   0.082923666   0.042073188   0.245128121   0.050620196 
##        NSI_Q6             P            Fu             E ABC_Tot_Score 
##   0.031735956   0.017132884   0.043260183   0.026079898   0.008534806 
##           FGA           SOT           LOC           AOC           PTA 
##   0.034753345   0.225801709   0.105449562   0.022912928   0.003382363 
## TimeInService      symp_tot 
##   0.002316847   0.028850331

Logistic Regression

  As shown in the previous tab, SOT, LOC, and NSI might explain some of the variance noticed within the Total C variable. We will reverse the analysis and look at the various groupings of Total C and compare their responses to these variables. Suggestive cutoff point would utilize 4 Total Central signs. Given the predictor variables: NSI Total Score (NSI Q2, NSI Q3), LOC, and SOT.

aov_data<-dhi%>%
  select('NSI_Q1','NSI_Q2','NSI_Q3','NSI_Q4','NSI_Q5','P','Fu','E','Total_C','NSI_Q6','ABC_Tot_Score','NSI_Tot_Score','PCLM_Tot_Score','PHQ9_Tot_Score','GAD7_Tot_Score','DHI_Tot_Score','FGA','SOT','LOC','AOC','PTA','TimeInService','symp_tot','Total_C','Age','Age_lbl')%>%
  filter(complete.cases(.))

auc<-NULL
for (i in 1:9){
  Cutoff<-paste0(quo_name(i),"")
  aov_data$I<-ifelse(aov_data$Total_C>=i,1,0)
  logic_fit<-glm(I~ABC_Tot_Score+NSI_Tot_Score+PCLM_Tot_Score+PHQ9_Tot_Score+GAD7_Tot_Score+DHI_Tot_Score+FGA+SOT+LOC+AOC+PTA+TimeInService+symp_tot,data=aov_data,family="binomial")
  prob<-predict(logic_fit,type=c("response"))
  aov_data$prob=prob
  g<-pROC::roc(I~prob,data=aov_data)
  #plot(g)
  a<-cbind(Cutoff,round(g$auc,4))
  auc<-rbind(auc,a)
}

#Full Model
print(auc)

##       Cutoff         
##  [1,] "1"    "0.6642"
##  [2,] "2"    "0.6328"
##  [3,] "3"    "0.6322"
##  [4,] "4"    "0.648" 
##  [5,] "5"    "0.6255"
##  [6,] "6"    "0.6504"
##  [7,] "7"    "0.7122"
##  [8,] "8"    "0.9557"
##  [9,] "9"    "0.9562"

auc<-NULL
for (i in 1:9){
  Cutoff<-paste0(quo_name(i),"")
  aov_data$I<-ifelse(aov_data$Total_C>=i,1,0)
  logic_fit<-glm(I~SOT+NSI_Tot_Score+LOC,data=aov_data,family="binomial")
  prob<-predict(logic_fit,type=c("response"))
  aov_data$prob=prob
  g<-pROC::roc(I~prob,data=aov_data)
  #plot(g)
  a<-cbind(Cutoff,round(g$auc,4))
  auc<-rbind(auc,a)
}

#Model:TOTAL_CENTRAL~SOT+NSI_Tot_Score+LOC
print(auc)

##       Cutoff         
##  [1,] "1"    "0.6248"
##  [2,] "2"    "0.626" 
##  [3,] "3"    "0.6001"
##  [4,] "4"    "0.6273"
##  [5,] "5"    "0.6161"
##  [6,] "6"    "0.6248"
##  [7,] "7"    "0.6834"
##  [8,] "8"    "0.7387"
##  [9,] "9"    "0.7535"

auc<-NULL
for (i in 1:9){
  Cutoff<-paste0(quo_name(i),"")
  aov_data$I<-ifelse(aov_data$Total_C>=i,1,0)
  logic_fit<-glm(I~SOT+NSI_Q2+NSI_Q4+LOC,data=aov_data,family="binomial")
  prob<-predict(logic_fit,type=c("response"))
  aov_data$prob=prob
  g<-pROC::roc(I~prob,data=aov_data)
  #plot(g)
  a<-cbind(Cutoff,round(g$auc,4))
  auc<-rbind(auc,a)
}

#Model:TOTAL_CENTRAL~SOT+NSI_Q2+NSI_Q4+LOC
print(auc)

##       Cutoff         
##  [1,] "1"    "0.6342"
##  [2,] "2"    "0.6483"
##  [3,] "3"    "0.6076"
##  [4,] "4"    "0.6486"
##  [5,] "5"    "0.6361"
##  [6,] "6"    "0.6453"
##  [7,] "7"    "0.7123"
##  [8,] "8"    "0.7509"
##  [9,] "9"    "0.7692"

AoV Testing

  We will now compare variable response between the various cutoffs of Central Dysfunction. P-values are provided which result from the MANOVA testing.

aov_list<-NULL
for (i in 1:9){
  aov_data$I<-ifelse(aov_data$Total_C>=i,"1","0")
  name<-paste0(quo_name(i),"")
  i<-round(summary(manova(cbind(ABC_Tot_Score,NSI_Tot_Score,PCLM_Tot_Score,PHQ9_Tot_Score,GAD7_Tot_Score,DHI_Tot_Score,FGA,SOT,LOC,AOC,PTA,symp_tot)~I,data=aov_data))$stats[11],5)
  test<-cbind(name,i)
  aov_list<-rbind(aov_list,test)}

#Model: ABC_Tot_Score,NSI_Tot_Score,PCLM_Tot_Score,PHQ9_Tot_Score,GAD7_Tot_Score,DHI_Tot_Score,FGA,SOT,LOC,AOC,PTA,symp_tot~TOTAL_CENTRAL
print(aov_list) 

##       name i        
##  [1,] "1"  "0.15953"
##  [2,] "2"  "0.06346"
##  [3,] "3"  "0.07946"
##  [4,] "4"  "0.07251"
##  [5,] "5"  "0.704"  
##  [6,] "6"  "0.80288"
##  [7,] "7"  "0.59811"
##  [8,] "8"  "0.01507"
##  [9,] "9"  "0.00187"

# 
# for (i in 1:9){
#   aov_data$I<-ifelse(aov_data$Total_C>=i,"1","0")
#   name<-paste0(quo_name(i),"")
#   i<-summary(manova(cbind(ABC_Tot_Score,NSI_Tot_Score,PCLM_Tot_Score,PHQ9_Tot_Score,GAD7_Tot_Score,DHI_Tot_Score,FGA,SOT,LOC,AOC,PTA,symp_tot)~I+Age,data=aov_data))
#   print(i)
# }

aov_list<-NULL
for (i in 1:9) {
  aov_data$I<-ifelse(aov_data$Total_C>=i,"1","0")
  name<-paste0(quo_name(i),"")
  i<-round(summary(manova(cbind(NSI_Tot_Score,SOT,LOC)~I,data=aov_data))$stats[11],5)
  test<-cbind(name,i)
  aov_list<-rbind(aov_list,test) }
#Model: NSI_Tot_Score+SOT+LOC~TOTAL_CENTRAL
print(aov_list) 

##       name i        
##  [1,] "1"  "0.01794"
##  [2,] "2"  "0.00195"
##  [3,] "3"  "0.00531"
##  [4,] "4"  "0.00183"
##  [5,] "5"  "0.1332" 
##  [6,] "6"  "0.15853"
##  [7,] "7"  "0.09409"
##  [8,] "8"  "0.0271" 
##  [9,] "9"  "0.00982"

aov_list<-NULL
for (i in 1:9) {
  aov_data$I<-ifelse(aov_data$Total_C>=i,"1","0")
  name<-paste0(quo_name(i),"")
  i<-round(summary(manova(cbind(NSI_Q2,NSI_Q4,SOT,LOC)~I,data=aov_data))$stats[11],5)
  test<-cbind(name,i)
  aov_list<-rbind(aov_list,test) }

#Model: NSI_Q2+NSI_Q4+SOT+LOC~TOTAL_CENTRAL
print(aov_list) 

##       name i        
##  [1,] "1"  "0.01578"
##  [2,] "2"  "0.00028"
##  [3,] "3"  "0.00765"
##  [4,] "4"  "5e-04"  
##  [5,] "5"  "0.01306"
##  [6,] "6"  "0.07586"
##  [7,] "7"  "0.16165"
##  [8,] "8"  "0.01618"
##  [9,] "9"  "0.01105"

Cutoffs and Specific Vestib Tests

  We will explore the population make ups utilizing 2:5 central cutoffs. We will compare their mean responses on our dataset.

Cutoff of 1

dhi$I<-ifelse(dhi$Total_C>=1,"1","0")
vestib<-dhi[,c(2:34,204)]
scores<-dhi[,c(37,38,66,81,85:90,107,142,161,70,201:204)]
dem<-dhi[,c(39:46,162:169,170,204)]

dm<-dem%>%
  filter(!is.na(I))%>%
  select(-c(Gender,Service,Special_stat,TypeParticipant,Motion_Sensitivity_Present:Syncope_pre_syncope_Present,Age_lbl))%>%
  group_by(I)%>%
  summarise_all(means)
kable(dm)

I	LOC	AOC	PTA	Age	TimeInService	symp_tot	med_ct
0	1.275862	2.150685	0.3241379	37.91558	17.55357	1.388889	0.3831169
1	1.335727	2.434783	0.3050542	39.09338	18.29219	1.629091	0.6808149

ss<-scores%>%
  filter(!is.na(I))%>%
  group_by(I)%>%
  summarise_all(means)

kable(ss)

I	SOT	FGA	ABC_Tot_Score	GAD7_Tot_Score	NSI_Q1	NSI_Q2	NSI_Q3	NSI_Q4	NSI_Q5	NSI_Q6	NSI_Tot_Score	PCLM_Tot_Score	PHQ9_Tot_Score	DHI_Tot_Score	P	Fu	E
0	84.23077	28.61240	86.40487	9.646018	0.8881579	0.8421053	0.8486842	1.684211	0.3377483	1.079470	31.48026	43.94040	9.66000	11.36913	4.517857	3.238938	3.646018
1	82.37575	27.55556	86.31502	10.857750	1.0963855	0.9948454	1.0706897	2.012007	0.5955250	1.343643	37.17496	46.84247	11.53108	18.76256	6.258873	5.462500	5.766667

colnames(vestib)<-sub("Vestibular_Oculomotor_","",colnames(vestib))
colnames(vestib)<-sub("Vestibular_Positioning_","",colnames(vestib))
colnames(vestib)<-sub("_Abnormal","",colnames(vestib))

vt<-vestib%>%
  filter(!is.na(I))%>%
  group_by(I)%>%
  summarise_all(funs(sum(.=="C",na.rm=TRUE)))

vt<-melt(vt)
vt$value<-as.numeric(vt$value)
o<-sum(dhi$I=="0")
i<-sum(dhi$I=="1")
vt$Mean<-ifelse(vt$I=="1",round(vt$value/i*100,3),round(vt$value/o*100,3))
vt<-vt%>%filter(!value==0)%>%arrange(-Mean)

kable(vt[,-1])

variable	value	Mean
Smooth_Pursuit_Vertical	481	81.664
Smooth_Pursuit_Horizontal	201	34.126
Saccades_Vertical	189	32.088
Vestibular_VNGTest_DixHallpike_Head_Right	127	21.562
Vestibular_VNGTest_DixHallpike_Head_Left	121	20.543
Vestibular_VNGTest_Roll_Test_Lateral_Left	119	20.204
Vestibular_VNGTest_Roll_Test_Lateral_Right	106	17.997
Gaze_Right	79	13.413
Gaze_Left	79	13.413
Saccades_Horizontal	75	12.733
GazeRL	75	12.733
Gaze_Up	44	7.470
Spon_Gaze	34	5.772
Gaze_Center	31	5.263
Gaze_Down	29	4.924
Optokinetic	23	3.905
Vestibular_VNGTest_Horizontal_Head_Shake	20	3.396
Spontaneous_Nystagmus	18	3.056
supine_Gaze_up	7	1.188
GazeR	4	0.679
GazeL	4	0.679
supine_Gaze_Center	1	0.170
Vestibular_VNGTest_Roll_Test_Body_Right	1	0.170

mnv<-manova(cbind(SOT,FGA, ABC_Tot_Score,DHI_Tot_Score,GAD7_Tot_Score, NSI_Tot_Score,NSI_Q2, NSI_Q3, NSI_Q4,NSI_Q5,NSI_Q6, PCLM_Tot_Score, PHQ9_Tot_Score, P, Fu)~I,data=scores)
summary(mnv)

##            Df   Pillai approx F num Df den Df Pr(>F)
## I           1 0.047007   1.4436     15    439 0.1231
## Residuals 453

summary.aov(mnv)

##  Response SOT :
##              Df  Sum Sq Mean Sq F value  Pr(>F)  
## I             1   163.8 163.757  5.3948 0.02064 *
## Residuals   453 13750.6  30.355                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
##  Response FGA :
##              Df  Sum Sq Mean Sq F value    Pr(>F)    
## I             1   46.88  46.883  12.993 0.0003473 ***
## Residuals   453 1634.52   3.608                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
##  Response ABC_Tot_Score :
##              Df Sum Sq Mean Sq F value Pr(>F)
## I             1    652  651.70  2.2953 0.1305
## Residuals   453 128619  283.93               
## 
##  Response DHI_Tot_Score :
##              Df Sum Sq Mean Sq F value   Pr(>F)   
## I             1   3729  3728.8  9.5296 0.002146 **
## Residuals   453 177250   391.3                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
##  Response GAD7_Tot_Score :
##              Df  Sum Sq Mean Sq F value  Pr(>F)  
## I             1   116.4 116.431  3.4639 0.06337 .
## Residuals   453 15226.7  33.613                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
##  Response NSI_Tot_Score :
##              Df Sum Sq Mean Sq F value   Pr(>F)   
## I             1   2461  2461.4  9.5776 0.002092 **
## Residuals   453 116421   257.0                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
##  Response NSI_Q2 :
##              Df Sum Sq Mean Sq F value  Pr(>F)  
## I             1   2.68 2.67766  3.7969 0.05197 .
## Residuals   453 319.47 0.70523                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
##  Response NSI_Q3 :
##              Df Sum Sq Mean Sq F value  Pr(>F)  
## I             1   4.47  4.4699  5.5115 0.01932 *
## Residuals   453 367.39  0.8110                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
##  Response NSI_Q4 :
##              Df Sum Sq Mean Sq F value   Pr(>F)   
## I             1  10.53 10.5289  8.6913 0.003363 **
## Residuals   453 548.78  1.2114                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
##  Response NSI_Q5 :
##              Df  Sum Sq Mean Sq F value  Pr(>F)  
## I             1   4.026  4.0259  6.0046 0.01465 *
## Residuals   453 303.724  0.6705                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
##  Response NSI_Q6 :
##              Df Sum Sq Mean Sq F value  Pr(>F)  
## I             1   4.52  4.5182  4.2963 0.03876 *
## Residuals   453 476.40  1.0516                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
##  Response PCLM_Tot_Score :
##              Df Sum Sq Mean Sq F value Pr(>F)  
## I             1   1617 1616.88  5.8697 0.0158 *
## Residuals   453 124785  275.46                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
##  Response PHQ9_Tot_Score :
##              Df  Sum Sq Mean Sq F value  Pr(>F)  
## I             1   202.9 202.935  5.4201 0.02035 *
## Residuals   453 16960.7  37.441                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
##  Response P :
##              Df  Sum Sq Mean Sq F value Pr(>F)   
## I             1   321.2  321.22  8.8425 0.0031 **
## Residuals   453 16456.0   36.33                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
##  Response Fu :
##              Df  Sum Sq Mean Sq F value   Pr(>F)   
## I             1   489.7  489.66  8.8624 0.003067 **
## Residuals   453 25028.6   55.25                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 288 observations deleted due to missingness

mnv<-manova(cbind(NSI_Q2,NSI_Q4,SOT,LOC)~I,data=dhi)
summary(mnv)

##            Df   Pillai approx F num Df den Df    Pr(>F)    
## I           1 0.032944   5.0843      4    597 0.0004922 ***
## Residuals 600                                              
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

summary.aov(mnv)

##  Response NSI_Q2 :
##              Df Sum Sq Mean Sq F value  Pr(>F)  
## I             1   2.88 2.88220  3.6225 0.05748 .
## Residuals   600 477.39 0.79564                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
##  Response NSI_Q4 :
##              Df Sum Sq Mean Sq F value    Pr(>F)    
## I             1  17.86  17.857  13.939 0.0002068 ***
## Residuals   600 768.63   1.281                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
##  Response SOT :
##              Df  Sum Sq Mean Sq F value   Pr(>F)   
## I             1   391.2  391.15  9.7708 0.001859 **
## Residuals   600 24019.6   40.03                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
##  Response LOC :
##              Df  Sum Sq Mean Sq F value Pr(>F)
## I             1    0.03 0.03248   0.018 0.8933
## Residuals   600 1082.97 1.80495               
## 
## 141 observations deleted due to missingness

Cutoff of 2

dhi$I<-ifelse(dhi$Total_C>=2,"1","0")
vestib<-dhi[,c(2:34,204)]
scores<-dhi[,c(37,38,66,81,85:90,107,142,161,70,201:204)]
dem<-dhi[,c(39:46,162:169,170,204)]

dm<-dem%>%
  filter(!is.na(I))%>%
  select(-c(Gender,Service,Special_stat,TypeParticipant,Motion_Sensitivity_Present:Syncope_pre_syncope_Present,Age_lbl))%>%
  group_by(I)%>%
  summarise_all(means)
kable(dm)

I	LOC	AOC	PTA	Age	TimeInService	symp_tot	med_ct
0	1.232082	2.325424	0.3071672	38.44795	17.86199	1.426117	0.4668770
1	1.388753	2.411911	0.3103448	39.14789	18.34531	1.689826	0.7323944

ss<-scores%>%
  filter(!is.na(I))%>%
  group_by(I)%>%
  summarise_all(means)

kable(ss)

I	SOT	FGA	ABC_Tot_Score	GAD7_Tot_Score	NSI_Q1	NSI_Q2	NSI_Q3	NSI_Q4	NSI_Q5	NSI_Q6	NSI_Tot_Score	PCLM_Tot_Score	PHQ9_Tot_Score	DHI_Tot_Score	P	Fu	E
0	84.18113	28.36538	87.24537	10.04938	0.9070513	0.8878205	0.9038462	1.753994	0.4102564	1.214744	33.36741	44.78526	10.32237	13.15686	4.987654	3.804082	4.269388
1	81.73098	27.34648	85.68182	11.03226	1.1615202	1.0189573	1.1142857	2.085308	0.6404762	1.344418	37.94787	47.32388	11.74328	20.22381	6.586207	5.908046	6.132184

colnames(vestib)<-sub("Vestibular_Oculomotor_","",colnames(vestib))
colnames(vestib)<-sub("Vestibular_Positioning_","",colnames(vestib))
colnames(vestib)<-sub("_Abnormal","",colnames(vestib))

vt<-vestib%>%
  filter(!is.na(I))%>%
  group_by(I)%>%
  summarise_all(funs(sum(.=="C",na.rm=TRUE)))

vt<-melt(vt)
vt$value<-as.numeric(vt$value)
o<-sum(dhi$I=="0")
i<-sum(dhi$I=="1")
vt$Mean<-ifelse(vt$I=="1",round(vt$value/i*100,3),round(vt$value/o*100,3))
vt<-vt%>%filter(!value==0)%>%arrange(-Mean)

kable(vt[,-1])

variable	value	Mean
Smooth_Pursuit_Vertical	355	83.333
Smooth_Pursuit_Horizontal	194	45.540
Saccades_Vertical	182	42.723
Smooth_Pursuit_Vertical	126	39.748
Vestibular_VNGTest_DixHallpike_Head_Right	122	28.638
Vestibular_VNGTest_DixHallpike_Head_Left	119	27.934
Vestibular_VNGTest_Roll_Test_Lateral_Left	116	27.230
Vestibular_VNGTest_Roll_Test_Lateral_Right	105	24.648
Gaze_Right	75	17.606
Gaze_Left	75	17.606
Saccades_Horizontal	73	17.136
GazeRL	71	16.667
Gaze_Up	42	9.859
Spon_Gaze	34	7.981
Gaze_Center	31	7.277
Gaze_Down	29	6.808
Optokinetic	23	5.399
Spontaneous_Nystagmus	18	4.225
Vestibular_VNGTest_Horizontal_Head_Shake	17	3.991
Smooth_Pursuit_Horizontal	7	2.208
Saccades_Vertical	7	2.208
Vestibular_VNGTest_DixHallpike_Head_Right	5	1.577
supine_Gaze_up	6	1.408
Gaze_Right	4	1.262
Gaze_Left	4	1.262
GazeRL	4	1.262
Vestibular_VNGTest_Horizontal_Head_Shake	3	0.946
Vestibular_VNGTest_Roll_Test_Lateral_Left	3	0.946
GazeR	4	0.939
GazeL	4	0.939
Saccades_Horizontal	2	0.631
Gaze_Up	2	0.631
Vestibular_VNGTest_DixHallpike_Head_Left	2	0.631
supine_Gaze_up	1	0.315
Vestibular_VNGTest_Roll_Test_Lateral_Right	1	0.315
supine_Gaze_Center	1	0.235
Vestibular_VNGTest_Roll_Test_Body_Right	1	0.235

mnv<-manova(cbind(SOT,FGA, ABC_Tot_Score,DHI_Tot_Score,GAD7_Tot_Score, NSI_Tot_Score,NSI_Q2, NSI_Q3, NSI_Q4,NSI_Q5,NSI_Q6, PCLM_Tot_Score, PHQ9_Tot_Score, P, Fu)~I,data=scores)
summary(mnv)

##            Df   Pillai approx F num Df den Df  Pr(>F)  
## I           1 0.060129   1.8723     15    439 0.02405 *
## Residuals 453                                          
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

summary.aov(mnv)

##  Response SOT :
##              Df Sum Sq Mean Sq F value   Pr(>F)   
## I             1    299 298.972  9.9471 0.001718 **
## Residuals   453  13615  30.056                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
##  Response FGA :
##              Df  Sum Sq Mean Sq F value   Pr(>F)   
## I             1   37.86  37.858  10.435 0.001327 **
## Residuals   453 1643.54   3.628                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
##  Response ABC_Tot_Score :
##              Df Sum Sq Mean Sq F value Pr(>F)
## I             1    355  355.27  1.2484 0.2645
## Residuals   453 128915  284.58               
## 
##  Response DHI_Tot_Score :
##              Df Sum Sq Mean Sq F value   Pr(>F)   
## I             1   2682 2682.30  6.8149 0.009339 **
## Residuals   453 178297  393.59                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
##  Response GAD7_Tot_Score :
##              Df  Sum Sq Mean Sq F value  Pr(>F)  
## I             1    92.4  92.364  2.7435 0.09834 .
## Residuals   453 15250.8  33.666                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
##  Response NSI_Tot_Score :
##              Df Sum Sq Mean Sq F value   Pr(>F)   
## I             1   2024 2024.04  7.8461 0.005311 **
## Residuals   453 116858  257.97                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
##  Response NSI_Q2 :
##              Df Sum Sq Mean Sq F value Pr(>F)
## I             1   1.62 1.61626  2.2842 0.1314
## Residuals   453 320.53 0.70757               
## 
##  Response NSI_Q3 :
##              Df Sum Sq Mean Sq F value  Pr(>F)  
## I             1   3.86  3.8570  4.7478 0.02985 *
## Residuals   453 368.00  0.8124                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
##  Response NSI_Q4 :
##              Df Sum Sq Mean Sq F value    Pr(>F)    
## I             1  13.23 13.2308  10.976 0.0009971 ***
## Residuals   453 546.07  1.2055                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
##  Response NSI_Q5 :
##              Df Sum Sq Mean Sq F value  Pr(>F)  
## I             1   2.86 2.86037  4.2499 0.03982 *
## Residuals   453 304.89 0.67304                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
##  Response NSI_Q6 :
##              Df Sum Sq Mean Sq F value Pr(>F)
## I             1   0.42 0.42491  0.4006 0.5271
## Residuals   453 480.49 1.06068               
## 
##  Response PCLM_Tot_Score :
##              Df Sum Sq Mean Sq F value  Pr(>F)  
## I             1   1272 1272.36  4.6062 0.03239 *
## Residuals   453 125130  276.22                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
##  Response PHQ9_Tot_Score :
##              Df  Sum Sq Mean Sq F value Pr(>F)  
## I             1   152.7 152.747  4.0676 0.0443 *
## Residuals   453 17010.9  37.552                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
##  Response P :
##              Df  Sum Sq Mean Sq F value   Pr(>F)   
## I             1   251.3 251.330  6.8893 0.008964 **
## Residuals   453 16525.9  36.481                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
##  Response Fu :
##              Df  Sum Sq Mean Sq F value   Pr(>F)   
## I             1   374.9  374.94  6.7552 0.009653 **
## Residuals   453 25143.3   55.50                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 288 observations deleted due to missingness

mnv<-manova(cbind(NSI_Q2,NSI_Q4,SOT,LOC)~I,data=dhi)
summary(mnv)

##            Df   Pillai approx F num Df den Df    Pr(>F)    
## I           1 0.057965   9.1835      4    597 3.323e-07 ***
## Residuals 600                                              
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

summary.aov(mnv)

##  Response NSI_Q2 :
##              Df Sum Sq Mean Sq F value Pr(>F)
## I             1   2.04 2.04246  2.5626 0.1099
## Residuals   600 478.22 0.79704               
## 
##  Response NSI_Q4 :
##              Df Sum Sq Mean Sq F value    Pr(>F)    
## I             1  22.93 22.9312  18.019 2.533e-05 ***
## Residuals   600 763.55  1.2726                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
##  Response SOT :
##              Df  Sum Sq Mean Sq F value  Pr(>F)    
## I             1   838.6  838.55  21.344 4.7e-06 ***
## Residuals   600 23572.2   39.29                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
##  Response LOC :
##              Df  Sum Sq Mean Sq F value Pr(>F)
## I             1    1.35  1.3465  0.7469 0.3878
## Residuals   600 1081.66  1.8028               
## 
## 141 observations deleted due to missingness

Cutoff of 3

dhi$I<-ifelse(dhi$Total_C>=3,"1","0")
vestib<-dhi[,c(2:34,204)]
scores<-dhi[,c(37,38,66,81,85:90,107,142,161,70,201:204)]
dem<-dhi[,c(39:46,162:169,170,204)]

dm<-dem%>%
  filter(!is.na(I))%>%
  select(-c(Gender,Service,Special_stat,TypeParticipant,Motion_Sensitivity_Present:Syncope_pre_syncope_Present,Age_lbl))%>%
  group_by(I)%>%
  summarise_all(means)
kable(dm)

I	LOC	AOC	PTA	Age	TimeInService	symp_tot	med_ct
0	1.259091	2.365079	0.2961276	38.84648	18.07644	1.489702	0.5458422
1	1.431298	2.392996	0.3307692	38.85401	18.24635	1.731518	0.7445255

ss<-scores%>%
  filter(!is.na(I))%>%
  group_by(I)%>%
  summarise_all(means)

kable(ss)

I	SOT	FGA	ABC_Tot_Score	GAD7_Tot_Score	NSI_Q1	NSI_Q2	NSI_Q3	NSI_Q4	NSI_Q5	NSI_Q6	NSI_Tot_Score	PCLM_Tot_Score	PHQ9_Tot_Score	DHI_Tot_Score	P	Fu	E
0	83.22959	28.01837	86.92240	10.33708	0.969697	0.9134199	0.9392625	1.835853	0.4826840	1.236443	34.19006	45.39741	10.70601	15.12473	5.582173	4.509695	4.886427
1	81.98755	27.38462	85.41118	11.07018	1.195572	1.0477941	1.1697417	2.128677	0.6444444	1.378677	39.07353	47.69118	11.87121	20.84758	6.465517	5.862069	6.103448

colnames(vestib)<-sub("Vestibular_Oculomotor_","",colnames(vestib))
colnames(vestib)<-sub("Vestibular_Positioning_","",colnames(vestib))
colnames(vestib)<-sub("_Abnormal","",colnames(vestib))

vt<-vestib%>%
  filter(!is.na(I))%>%
  group_by(I)%>%
  summarise_all(funs(sum(.=="C",na.rm=TRUE)))

vt<-melt(vt)
vt$value<-as.numeric(vt$value)
o<-sum(dhi$I=="0")
i<-sum(dhi$I=="1")
vt$Mean<-ifelse(vt$I=="1",round(vt$value/i*100,3),round(vt$value/o*100,3))
vt<-vt%>%filter(!value==0)%>%arrange(-Mean)

kable(vt[,-1])

variable	value	Mean
Smooth_Pursuit_Vertical	238	86.861
Saccades_Vertical	142	51.825
Smooth_Pursuit_Vertical	243	51.812
Smooth_Pursuit_Horizontal	135	49.270
Vestibular_VNGTest_DixHallpike_Head_Right	108	39.416
Vestibular_VNGTest_Roll_Test_Lateral_Left	106	38.686
Vestibular_VNGTest_DixHallpike_Head_Left	104	37.956
Vestibular_VNGTest_Roll_Test_Lateral_Right	97	35.401
Saccades_Horizontal	62	22.628
Gaze_Right	61	22.263
Gaze_Left	61	22.263
GazeRL	57	20.803
Gaze_Up	41	14.964
Smooth_Pursuit_Horizontal	66	14.072
Spon_Gaze	31	11.314
Gaze_Center	30	10.949
Saccades_Vertical	47	10.021
Gaze_Down	27	9.854
Optokinetic	18	6.569
Spontaneous_Nystagmus	16	5.839
Vestibular_VNGTest_Horizontal_Head_Shake	12	4.380
Vestibular_VNGTest_DixHallpike_Head_Right	19	4.051
Gaze_Right	18	3.838
Gaze_Left	18	3.838
GazeRL	18	3.838
Vestibular_VNGTest_DixHallpike_Head_Left	17	3.625
Saccades_Horizontal	13	2.772
Vestibular_VNGTest_Roll_Test_Lateral_Left	13	2.772
supine_Gaze_up	6	2.190
Vestibular_VNGTest_Roll_Test_Lateral_Right	9	1.919
Vestibular_VNGTest_Horizontal_Head_Shake	8	1.706
GazeR	4	1.460
GazeL	4	1.460
Optokinetic	5	1.066
Gaze_Up	3	0.640
Spon_Gaze	3	0.640
Spontaneous_Nystagmus	2	0.426
Gaze_Down	2	0.426
supine_Gaze_Center	1	0.365
Vestibular_VNGTest_Roll_Test_Body_Right	1	0.365
Gaze_Center	1	0.213
supine_Gaze_up	1	0.213

mnv<-manova(cbind(SOT,FGA, ABC_Tot_Score,DHI_Tot_Score,GAD7_Tot_Score, NSI_Tot_Score,NSI_Q2, NSI_Q3, NSI_Q4,NSI_Q5,NSI_Q6, PCLM_Tot_Score, PHQ9_Tot_Score, P, Fu)~I,data=scores)
summary(mnv)

##            Df   Pillai approx F num Df den Df Pr(>F)
## I           1 0.042293   1.2924     15    439 0.2026
## Residuals 453

summary.aov(mnv)

##  Response SOT :
##              Df  Sum Sq Mean Sq F value Pr(>F)
## I             1    41.4  41.357  1.3505 0.2458
## Residuals   453 13873.0  30.625               
## 
##  Response FGA :
##              Df Sum Sq Mean Sq F value Pr(>F)
## I             1    8.7  8.7049  2.3575 0.1254
## Residuals   453 1672.7  3.6925               
## 
##  Response ABC_Tot_Score :
##              Df Sum Sq Mean Sq F value Pr(>F)
## I             1    600  599.70  2.1113 0.1469
## Residuals   453 128671  284.04               
## 
##  Response DHI_Tot_Score :
##              Df Sum Sq Mean Sq F value Pr(>F)
## I             1    763  762.66  1.9171 0.1669
## Residuals   453 180216  397.83               
## 
##  Response GAD7_Tot_Score :
##              Df  Sum Sq Mean Sq F value Pr(>F)
## I             1    37.9  37.867  1.1208 0.2903
## Residuals   453 15305.3  33.787               
## 
##  Response NSI_Tot_Score :
##              Df Sum Sq Mean Sq F value   Pr(>F)   
## I             1   2294 2294.09  8.9136 0.002984 **
## Residuals   453 116588  257.37                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
##  Response NSI_Q2 :
##              Df Sum Sq Mean Sq F value Pr(>F)
## I             1   1.09 1.08669  1.5333 0.2163
## Residuals   453 321.06 0.70874               
## 
##  Response NSI_Q3 :
##              Df Sum Sq Mean Sq F value  Pr(>F)  
## I             1   2.71  2.7098  3.3253 0.06888 .
## Residuals   453 369.15  0.8149                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
##  Response NSI_Q4 :
##              Df Sum Sq Mean Sq F value   Pr(>F)   
## I             1  10.30 10.2995  8.4984 0.003731 **
## Residuals   453 549.01  1.2119                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
##  Response NSI_Q5 :
##              Df  Sum Sq Mean Sq F value  Pr(>F)  
## I             1   3.548  3.5479  5.2833 0.02198 *
## Residuals   453 304.202  0.6715                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
##  Response NSI_Q6 :
##              Df Sum Sq Mean Sq F value Pr(>F)
## I             1   2.62  2.6240  2.4853 0.1156
## Residuals   453 478.29  1.0558               
## 
##  Response PCLM_Tot_Score :
##              Df Sum Sq Mean Sq F value Pr(>F)
## I             1    515  514.51  1.8514 0.1743
## Residuals   453 125888  277.90               
## 
##  Response PHQ9_Tot_Score :
##              Df Sum Sq Mean Sq F value Pr(>F)
## I             1     53  53.040  1.4042 0.2366
## Residuals   453  17111  37.772               
## 
##  Response P :
##              Df  Sum Sq Mean Sq F value Pr(>F)
## I             1    91.7  91.691  2.4893 0.1153
## Residuals   453 16685.5  36.833               
## 
##  Response Fu :
##              Df  Sum Sq Mean Sq F value Pr(>F)
## I             1   115.4 115.358  2.0571 0.1522
## Residuals   453 25402.9  56.077               
## 
## 288 observations deleted due to missingness

mnv<-manova(cbind(NSI_Q2,NSI_Q4,SOT,LOC)~I,data=dhi)
summary(mnv)

##            Df   Pillai approx F num Df den Df   Pr(>F)   
## I           1 0.029418   4.5237      4    597 0.001317 **
## Residuals 600                                            
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

summary.aov(mnv)

##  Response NSI_Q2 :
##              Df Sum Sq Mean Sq F value  Pr(>F)  
## I             1   2.54 2.54113  3.1915 0.07453 .
## Residuals   600 477.73 0.79621                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
##  Response NSI_Q4 :
##              Df Sum Sq Mean Sq F value    Pr(>F)    
## I             1  15.24 15.2380  11.854 0.0006153 ***
## Residuals   600 771.25  1.2854                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
##  Response SOT :
##              Df  Sum Sq Mean Sq F value  Pr(>F)  
## I             1   216.7 216.679  5.3735 0.02078 *
## Residuals   600 24194.0  40.323                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
##  Response LOC :
##              Df  Sum Sq Mean Sq F value  Pr(>F)  
## I             1    6.14  6.1402  3.4212 0.06486 .
## Residuals   600 1076.86  1.7948                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 141 observations deleted due to missingness

Cutoff of 4

dhi$I<-ifelse(dhi$Total_C>=4,"1","0")
vestib<-dhi[,c(2:34,204)]
scores<-dhi[,c(37,38,66,81,85:90,107,142,161,70,201:204)]
dem<-dhi[,c(39:46,162:169,170,204)]

dm<-dem%>%
  filter(!is.na(I))%>%
  select(-c(Gender,Service,Special_stat,TypeParticipant,Motion_Sensitivity_Present:Syncope_pre_syncope_Present,Age_lbl))%>%
  group_by(I)%>%
  summarise_all(means)
kable(dm)

I	LOC	AOC	PTA	Age	TimeInService	symp_tot	med_ct
0	1.270522	2.368224	0.3158879	38.78070	18.06553	1.538606	0.5631579
1	1.493976	2.398773	0.2865854	39.07514	18.38150	1.711656	0.8034682

ss<-scores%>%
  filter(!is.na(I))%>%
  group_by(I)%>%
  summarise_all(means)

kable(ss)

I	SOT	FGA	ABC_Tot_Score	GAD7_Tot_Score	NSI_Q1	NSI_Q2	NSI_Q3	NSI_Q4	NSI_Q5	NSI_Q6	NSI_Tot_Score	PCLM_Tot_Score	PHQ9_Tot_Score	DHI_Tot_Score	P	Fu	E
0	83.08524	27.91006	86.81935	10.39498	1.005329	0.9431616	0.9839858	1.877660	0.5186501	1.272242	34.87943	45.52837	10.80403	15.88829	5.796840	4.606742	4.943820
1	81.71711	27.35811	84.87158	11.30822	1.211765	1.0292398	1.1588235	2.163743	0.6213018	1.345029	39.68421	48.61404	12.22754	21.64912	6.324324	6.337838	6.621622

colnames(vestib)<-sub("Vestibular_Oculomotor_","",colnames(vestib))
colnames(vestib)<-sub("Vestibular_Positioning_","",colnames(vestib))
colnames(vestib)<-sub("_Abnormal","",colnames(vestib))

vt<-vestib%>%
  filter(!is.na(I))%>%
  group_by(I)%>%
  summarise_all(funs(sum(.=="C",na.rm=TRUE)))

vt<-melt(vt)
vt$value<-as.numeric(vt$value)
o<-sum(dhi$I=="0")
i<-sum(dhi$I=="1")
vt$Mean<-ifelse(vt$I=="1",round(vt$value/i*100,3),round(vt$value/o*100,3))
vt<-vt%>%filter(!value==0)%>%arrange(-Mean)

kable(vt[,-1])

variable	value	Mean
Smooth_Pursuit_Vertical	152	87.861
Smooth_Pursuit_Vertical	329	57.719
Smooth_Pursuit_Horizontal	94	54.335
Saccades_Vertical	91	52.601
Vestibular_VNGTest_Roll_Test_Lateral_Left	89	51.445
Vestibular_VNGTest_DixHallpike_Head_Right	85	49.133
Vestibular_VNGTest_Roll_Test_Lateral_Right	82	47.399
Vestibular_VNGTest_DixHallpike_Head_Left	80	46.243
Gaze_Right	48	27.746
Gaze_Left	48	27.746
Saccades_Horizontal	47	27.168
GazeRL	44	25.434
Gaze_Up	37	21.387
Smooth_Pursuit_Horizontal	107	18.772
Spon_Gaze	30	17.341
Saccades_Vertical	98	17.193
Gaze_Center	29	16.763
Gaze_Down	25	14.451
Spontaneous_Nystagmus	15	8.671
Optokinetic	15	8.671
Vestibular_VNGTest_DixHallpike_Head_Right	42	7.368
Vestibular_VNGTest_DixHallpike_Head_Left	41	7.193
Gaze_Right	31	5.439
Gaze_Left	31	5.439
GazeRL	31	5.439
Vestibular_VNGTest_Roll_Test_Lateral_Left	30	5.263
Saccades_Horizontal	28	4.912
Vestibular_VNGTest_Roll_Test_Lateral_Right	24	4.211
Vestibular_VNGTest_Horizontal_Head_Shake	7	4.046
supine_Gaze_up	4	2.312
GazeR	4	2.312
GazeL	4	2.312
Vestibular_VNGTest_Horizontal_Head_Shake	13	2.281
Optokinetic	8	1.404
Gaze_Up	7	1.228
Gaze_Down	4	0.702
Spon_Gaze	4	0.702
supine_Gaze_Center	1	0.578
Spontaneous_Nystagmus	3	0.526
supine_Gaze_up	3	0.526
Gaze_Center	2	0.351
Vestibular_VNGTest_Roll_Test_Body_Right	1	0.175

mnv<-manova(cbind(SOT,FGA, ABC_Tot_Score,DHI_Tot_Score,GAD7_Tot_Score, NSI_Tot_Score,NSI_Q2, NSI_Q3, NSI_Q4,NSI_Q5,NSI_Q6, PCLM_Tot_Score, PHQ9_Tot_Score, P, Fu)~I,data=scores)
summary(mnv)

##            Df   Pillai approx F num Df den Df Pr(>F)
## I           1 0.048818   1.5021     15    439 0.1003
## Residuals 453

summary.aov(mnv)

##  Response SOT :
##              Df  Sum Sq Mean Sq F value Pr(>F)
## I             1    79.1  79.053  2.5884 0.1083
## Residuals   453 13835.3  30.541               
## 
##  Response FGA :
##              Df  Sum Sq Mean Sq F value Pr(>F)
## I             1    7.15  7.1525  1.9352 0.1649
## Residuals   453 1674.25  3.6959               
## 
##  Response ABC_Tot_Score :
##              Df Sum Sq Mean Sq F value  Pr(>F)  
## I             1    883  883.22  3.1163 0.07818 .
## Residuals   453 128387  283.42                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
##  Response DHI_Tot_Score :
##              Df Sum Sq Mean Sq F value  Pr(>F)  
## I             1   1598 1597.93  4.0353 0.04515 *
## Residuals   453 179381  395.98                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
##  Response GAD7_Tot_Score :
##              Df  Sum Sq Mean Sq F value Pr(>F)
## I             1    78.4  78.427  2.3274 0.1278
## Residuals   453 15264.7  33.697               
## 
##  Response NSI_Tot_Score :
##              Df Sum Sq Mean Sq F value   Pr(>F)   
## I             1   2107 2106.86   8.173 0.004448 **
## Residuals   453 116776  257.78                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
##  Response NSI_Q2 :
##              Df Sum Sq Mean Sq F value Pr(>F)
## I             1   0.97 0.96613  1.3627 0.2437
## Residuals   453 321.18 0.70900               
## 
##  Response NSI_Q3 :
##              Df Sum Sq Mean Sq F value  Pr(>F)  
## I             1   3.34  3.3359  4.1005 0.04346 *
## Residuals   453 368.52  0.8135                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
##  Response NSI_Q4 :
##              Df Sum Sq Mean Sq F value    Pr(>F)    
## I             1  13.78 13.7850  11.447 0.0007783 ***
## Residuals   453 545.52  1.2042                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
##  Response NSI_Q5 :
##              Df  Sum Sq Mean Sq F value  Pr(>F)  
## I             1   2.619 2.61914  3.8884 0.04923 *
## Residuals   453 305.130 0.67358                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
##  Response NSI_Q6 :
##              Df Sum Sq Mean Sq F value Pr(>F)
## I             1   0.37 0.36618  0.3452 0.5571
## Residuals   453 480.55 1.06081               
## 
##  Response PCLM_Tot_Score :
##              Df Sum Sq Mean Sq F value Pr(>F)
## I             1    655  654.92  2.3593 0.1252
## Residuals   453 125747  277.59               
## 
##  Response PHQ9_Tot_Score :
##              Df  Sum Sq Mean Sq F value  Pr(>F)  
## I             1   114.2 114.163  3.0333 0.08225 .
## Residuals   453 17049.5  37.637                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
##  Response P :
##              Df  Sum Sq Mean Sq F value Pr(>F)
## I             1    53.1  53.147  1.4396 0.2308
## Residuals   453 16724.1  36.919               
## 
##  Response Fu :
##              Df  Sum Sq Mean Sq F value  Pr(>F)  
## I             1   316.6  316.58  5.6905 0.01747 *
## Residuals   453 25201.7   55.63                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 288 observations deleted due to missingness

mnv<-manova(cbind(NSI_Q2,NSI_Q4,SOT,LOC)~I,data=dhi)
summary(mnv)

##            Df   Pillai approx F num Df den Df    Pr(>F)    
## I           1 0.030733   4.7324      4    597 0.0009137 ***
## Residuals 600                                              
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

summary.aov(mnv)

##  Response NSI_Q2 :
##              Df Sum Sq Mean Sq F value Pr(>F)
## I             1   1.80 1.79501  2.2509 0.1341
## Residuals   600 478.47 0.79745               
## 
##  Response NSI_Q4 :
##              Df Sum Sq Mean Sq F value   Pr(>F)    
## I             1  14.61 14.6070  11.354 0.000801 ***
## Residuals   600 771.88  1.2865                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
##  Response SOT :
##              Df  Sum Sq Mean Sq F value  Pr(>F)  
## I             1   249.5 249.467  6.1951 0.01308 *
## Residuals   600 24161.3  40.269                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
##  Response LOC :
##              Df  Sum Sq Mean Sq F value  Pr(>F)  
## I             1    6.64  6.6378  3.7001 0.05488 .
## Residuals   600 1076.37  1.7939                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 141 observations deleted due to missingness

Cutoff of 5

dhi$I<-ifelse(dhi$Total_C>=5,"1","0")
vestib<-dhi[,c(2:34,204)]
scores<-dhi[,c(37,38,66,81,85:90,107,142,161,70,201:204)]
dem<-dhi[,c(39:46,162:169,170,204)]

dm<-dem%>%
  filter(!is.na(I))%>%
  select(-c(Gender,Service,Special_stat,TypeParticipant,Motion_Sensitivity_Present:Syncope_pre_syncope_Present,Age_lbl))%>%
  group_by(I)%>%
  summarise_all(means)
kable(dm)

I	LOC	AOC	PTA	Age	TimeInService	symp_tot	med_ct
0	1.299501	2.328882	0.312187	38.96081	18.25133	1.557983	0.5940439
1	1.465347	2.656566	0.290000	38.17143	17.45714	1.707071	0.7714286

ss<-scores%>%
  filter(!is.na(I))%>%
  group_by(I)%>%
  summarise_all(means)

kable(ss)

I	SOT	FGA	ABC_Tot_Score	GAD7_Tot_Score	NSI_Q1	NSI_Q2	NSI_Q3	NSI_Q4	NSI_Q5	NSI_Q6	NSI_Tot_Score	PCLM_Tot_Score	PHQ9_Tot_Score	DHI_Tot_Score	P	Fu	E
0	82.87132	27.78748	86.59934	10.43699	1.027027	0.9634921	1.012719	1.914422	0.5389507	1.273450	35.54992	45.89223	10.95417	16.79100	5.847390	4.848000	5.160000
1	82.05618	27.71591	84.90489	11.61957	1.211539	0.9615385	1.097087	2.125000	0.5631068	1.384615	38.71154	48.39423	12.23529	19.96154	6.365591	6.064516	6.451613

colnames(vestib)<-sub("Vestibular_Oculomotor_","",colnames(vestib))
colnames(vestib)<-sub("Vestibular_Positioning_","",colnames(vestib))
colnames(vestib)<-sub("_Abnormal","",colnames(vestib))

vt<-vestib%>%
  filter(!is.na(I))%>%
  group_by(I)%>%
  summarise_all(funs(sum(.=="C",na.rm=TRUE)))

vt<-melt(vt)
vt$value<-as.numeric(vt$value)
o<-sum(dhi$I=="0")
i<-sum(dhi$I=="1")
vt$Mean<-ifelse(vt$I=="1",round(vt$value/i*100,3),round(vt$value/o*100,3))
vt<-vt%>%filter(!value==0)%>%arrange(-Mean)

kable(vt[,-1])

variable	value	Mean
Smooth_Pursuit_Vertical	96	91.429
Vestibular_VNGTest_Roll_Test_Lateral_Left	64	60.952
Smooth_Pursuit_Vertical	385	60.345
Vestibular_VNGTest_Roll_Test_Lateral_Right	61	58.095
Saccades_Vertical	60	57.143
Smooth_Pursuit_Horizontal	58	55.238
Vestibular_VNGTest_DixHallpike_Head_Right	58	55.238
Vestibular_VNGTest_DixHallpike_Head_Left	56	53.333
Gaze_Right	37	35.238
Gaze_Left	36	34.286
GazeRL	33	31.429
Gaze_Up	30	28.571
Saccades_Horizontal	29	27.619
Spon_Gaze	25	23.810
Gaze_Center	24	22.857
Smooth_Pursuit_Horizontal	143	22.414
Saccades_Vertical	129	20.219
Gaze_Down	21	20.000
Spontaneous_Nystagmus	14	13.333
Vestibular_VNGTest_DixHallpike_Head_Right	69	10.815
Vestibular_VNGTest_DixHallpike_Head_Left	65	10.188
Optokinetic	10	9.524
Vestibular_VNGTest_Roll_Test_Lateral_Left	55	8.621
Saccades_Horizontal	46	7.210
Vestibular_VNGTest_Roll_Test_Lateral_Right	45	7.053
Gaze_Left	43	6.740
Gaze_Right	42	6.583
GazeRL	42	6.583
Vestibular_VNGTest_Horizontal_Head_Shake	6	5.714
supine_Gaze_up	4	3.810
GazeR	4	3.810
GazeL	3	2.857
Gaze_Up	14	2.194
Vestibular_VNGTest_Horizontal_Head_Shake	14	2.194
Optokinetic	13	2.038
Spon_Gaze	9	1.411
Gaze_Down	8	1.254
Gaze_Center	7	1.097
supine_Gaze_Center	1	0.952
Spontaneous_Nystagmus	4	0.627
supine_Gaze_up	3	0.470
Vestibular_VNGTest_Roll_Test_Body_Right	1	0.157
GazeL	1	0.157

mnv<-manova(cbind(SOT,FGA, ABC_Tot_Score,DHI_Tot_Score,GAD7_Tot_Score, NSI_Tot_Score,NSI_Q2, NSI_Q3, NSI_Q4,NSI_Q5,NSI_Q6, PCLM_Tot_Score, PHQ9_Tot_Score, P, Fu)~I,data=scores)
summary(mnv)

##            Df  Pillai approx F num Df den Df Pr(>F)
## I           1 0.03491   1.0587     15    439 0.3935
## Residuals 453

summary.aov(mnv)

##  Response SOT :
##              Df  Sum Sq Mean Sq F value Pr(>F)
## I             1    61.2  61.218  2.0018 0.1578
## Residuals   453 13853.1  30.581               
## 
##  Response FGA :
##              Df  Sum Sq Mean Sq F value Pr(>F)
## I             1    0.31  0.3062  0.0825 0.7741
## Residuals   453 1681.09  3.7110               
## 
##  Response ABC_Tot_Score :
##              Df Sum Sq Mean Sq F value  Pr(>F)  
## I             1    852  852.12  3.0059 0.08364 .
## Residuals   453 128419  283.48                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
##  Response DHI_Tot_Score :
##              Df Sum Sq Mean Sq F value Pr(>F)
## I             1    562  561.63  1.4102 0.2357
## Residuals   453 180417  398.27               
## 
##  Response GAD7_Tot_Score :
##              Df  Sum Sq Mean Sq F value  Pr(>F)  
## I             1    97.9  97.931  2.9099 0.08872 .
## Residuals   453 15245.2  33.654                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
##  Response NSI_Tot_Score :
##              Df Sum Sq Mean Sq F value Pr(>F)  
## I             1    751  751.38  2.8813 0.0903 .
## Residuals   453 118131  260.78                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
##  Response NSI_Q2 :
##              Df Sum Sq Mean Sq F value Pr(>F)
## I             1   0.00 0.00477  0.0067 0.9348
## Residuals   453 322.14 0.71113               
## 
##  Response NSI_Q3 :
##              Df Sum Sq Mean Sq F value Pr(>F)
## I             1   1.12 1.11958   1.368 0.2428
## Residuals   453 370.74 0.81841               
## 
##  Response NSI_Q4 :
##              Df Sum Sq Mean Sq F value   Pr(>F)   
## I             1   8.78  8.7772  7.2223 0.007466 **
## Residuals   453 550.53  1.2153                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
##  Response NSI_Q5 :
##              Df  Sum Sq Mean Sq F value Pr(>F)
## I             1   1.201 1.20122  1.7751 0.1834
## Residuals   453 306.548 0.67671               
## 
##  Response NSI_Q6 :
##              Df Sum Sq Mean Sq F value Pr(>F)
## I             1   0.01 0.00971  0.0091 0.9239
## Residuals   453 480.90 1.06160               
## 
##  Response PCLM_Tot_Score :
##              Df Sum Sq Mean Sq F value Pr(>F)
## I             1    566  565.51  2.0358 0.1543
## Residuals   453 125837  277.78               
## 
##  Response PHQ9_Tot_Score :
##              Df  Sum Sq Mean Sq F value Pr(>F)
## I             1    94.4  94.429   2.506 0.1141
## Residuals   453 17069.3  37.680               
## 
##  Response P :
##              Df  Sum Sq Mean Sq F value Pr(>F)
## I             1    25.1  25.112  0.6791 0.4103
## Residuals   453 16752.1  36.980               
## 
##  Response Fu :
##              Df  Sum Sq Mean Sq F value Pr(>F)
## I             1    87.2  87.207  1.5534 0.2133
## Residuals   453 25431.1  56.139               
## 
## 288 observations deleted due to missingness

mnv<-manova(cbind(NSI_Q2,NSI_Q4,SOT,LOC)~I,data=dhi)
summary(mnv)

##            Df   Pillai approx F num Df den Df Pr(>F)
## I           1 0.012892   1.9493      4    597 0.1008
## Residuals 600

summary.aov(mnv)

##  Response NSI_Q2 :
##              Df Sum Sq Mean Sq F value Pr(>F)
## I             1   0.01 0.01467  0.0183 0.8924
## Residuals   600 480.25 0.80042               
## 
##  Response NSI_Q4 :
##              Df Sum Sq Mean Sq F value Pr(>F)  
## I             1   5.21  5.2064  3.9983  0.046 *
## Residuals   600 781.28  1.3021                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
##  Response SOT :
##              Df  Sum Sq Mean Sq F value Pr(>F)
## I             1    69.1  69.098  1.7032 0.1924
## Residuals   600 24341.6  40.569               
## 
##  Response LOC :
##              Df  Sum Sq Mean Sq F value Pr(>F)
## I             1    1.25  1.2493  0.6929 0.4055
## Residuals   600 1081.76  1.8029               
## 
## 141 observations deleted due to missingness

Medications

Comments/Questions:

Prior Percentage of patients with central dysfunction Particular test thats significant of central signs <Explore individuals signs - those with our cutoff score Control for Medications - subset data with AM/PM <Significance of either

Meeting Comments: Fix complaints/comments Medication and compare - look at counts as well Finalize Mean Comparisons - tukey/scheffe test; examine graphs - main aspect of paper